A method for diagnosing rolling bearing faults based on SDAE-ADHKELM

Abstract

Aiming to extract useful features from bearing signals for fault classification, an intelligent fault diagnosis method is proposed with a stacked denoising auto-encoder (SDAE) and adaptive deep hybrid kernel extreme learning machine (ADHKELM). The deep network architecture of the SDAE is used automatically to extract deeply important features, and a new HKELM is constructed by combining a polynomial with a wavelet kernel function to overcome a single kernel function not being universal. After that a DHKELM, from stacking multiple HKELMs, and the sparrow search algorithm are introduced to iteratively determine the optimal value of core hyper-parameter combinations of the DHKELM to generate the final fault classifier ADHKELM to enhance the performance of the model. Two experimental verification results show that the SDAE-ADHKELM has better fault classification precision, robustness and generalizability than other related methods.